In this paper, the modular combination of artificial neural nets is considered. A modular approach to combining can be contrasted with an ensemble-based approach in that it implies individual modules, each responsible for some specialist aspect of a task, as opposed to each approximating the same function. It is possible to characterize modular systems in terms of (i) reasons for the task decomposition, (ii) the method for accomplishing the decomposition and (iii) the relationship between the modules. These characteristics are considered in brief outlines of the papers in the issue. Reasons for task decomposition include the exploitation of specialist capabilities of individual nets, performance improvement, and making the system easier to understand and modify. Task decomposition may be either automatic (based on the blind application of a data partitioning algorithm) or explicit (based on prior knowledge of the task or the specialist capabilities of the modules), and the relationship between the modules may be successive, cooperative or supervisory.